Manifold Preserving: An Intrinsic Approach for Semisupervised Distance Metric Learning
Shihui Ying; Zhijie Wen; Jun Shi; Yaxin Peng; Jigen Peng; Hong Qiao
2017
发表期刊IEEE Transactions on Neural Networks and Learning Systems
卷号PP期号:99页码:1-12
摘要In this paper, we address the semisupervised distance metric learning problem and its applications in classification and image retrieval. First, we formulate a semisupervised distance metric learning model by considering the metric information of inner classes and interclasses. In this model, an adaptive parameter is designed to balance the inner metrics and intermetrics by using data structure. Second, we convert the model to a minimization problem whose variable is symmetric positive-definite matrix. Third, in implementation, we deduce an intrinsic steepest descent method, which assures that the metric matrix is strictly symmetric positive-definite at each iteration, with the manifold structure of the symmetric positivedefinite matrix manifold. Finally, we test the proposed algorithm on conventional data sets, and compare it with other four representative methods. The numerical results validate that the proposed method significantly improves the classification with the same computational efficiency.
关键词Classification Distance Metric Learning Intrinsic Algorithm Matrix Manifold Semisupervised Learning.
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/20103
专题复杂系统管理与控制国家重点实验室_机器人理论与应用
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GB/T 7714
Shihui Ying,Zhijie Wen,Jun Shi,et al. Manifold Preserving: An Intrinsic Approach for Semisupervised Distance Metric Learning[J]. IEEE Transactions on Neural Networks and Learning Systems,2017,PP(99):1-12.
APA Shihui Ying,Zhijie Wen,Jun Shi,Yaxin Peng,Jigen Peng,&Hong Qiao.(2017).Manifold Preserving: An Intrinsic Approach for Semisupervised Distance Metric Learning.IEEE Transactions on Neural Networks and Learning Systems,PP(99),1-12.
MLA Shihui Ying,et al."Manifold Preserving: An Intrinsic Approach for Semisupervised Distance Metric Learning".IEEE Transactions on Neural Networks and Learning Systems PP.99(2017):1-12.
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